



'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow

class 							a__:






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Union[str, Any]:
					return None





class 							a__:






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Union[str, Any]:
					return None





class 							a__( unittest.TestCase     ):
			a_	: Tuple									=      [
			    # (model_name, model_kwargs)
			    ('''bert-base-cased''', {}),
			    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
			]






			@require_tf
			@slow
			def 						_lowercase  (     self						)							->							Tuple:
					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							self._test_export(_UpperCAmelCase					,   'tf'					,   12					,   **_UpperCAmelCase						)






			@require_torch
			@slow
			def 						_lowercase  (     self						)							->							Dict:
					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							self._test_export(_UpperCAmelCase					,   'pt'					,   12					,   **_UpperCAmelCase						)






			@require_torch
			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					from transformers import BertModel

					snake_case__	     =['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
					with NamedTemporaryFile(mode='w+t'						) as vocab_file:
							vocab_file.write('\n'.join(_UpperCAmelCase						)						)
							vocab_file.flush()
							snake_case__	     =BertTokenizerFast(vocab_file.name						)

					with TemporaryDirectory() as bert_save_dir:
							snake_case__	     =BertModel(BertConfig(vocab_size=len(_UpperCAmelCase						)						)						)
							model.save_pretrained(_UpperCAmelCase						)
							self._test_export(_UpperCAmelCase					,   'pt'					,   12					,   _UpperCAmelCase						)






			@require_tf
			@slow
			def 						_lowercase  (     self						)							->							Optional[int]:
					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							snake_case__	     =self._test_export(_UpperCAmelCase					,   'tf'					,   12					,   **_UpperCAmelCase						)
							snake_case__	     =quantize(Path(_UpperCAmelCase						)						)

							# Ensure the actual quantized model is not bigger than the original one
							if quantized_path.stat().st_size >= Path(_UpperCAmelCase						).stat().st_size:
									self.fail('Quantized model is bigger than initial ONNX model'						)






			@require_torch
			@slow
			def 						_lowercase  (     self						)							->							Tuple:
					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							snake_case__	     =self._test_export(_UpperCAmelCase					,   'pt'					,   12					,   **_UpperCAmelCase						)
							snake_case__	     =quantize(_UpperCAmelCase						)

							# Ensure the actual quantized model is not bigger than the original one
							if quantized_path.stat().st_size >= Path(_UpperCAmelCase						).stat().st_size:
									self.fail('Quantized model is bigger than initial ONNX model'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   **_UpperCAmelCase						)							->							Optional[int]:
					try:
							# Compute path
							with TemporaryDirectory() as tempdir:
									snake_case__	     =Path(_UpperCAmelCase						).joinpath('model.onnx'						)

							# Remove folder if exists
							if path.parent.exists():
									path.parent.rmdir()

							# Export
							convert(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)

							return path
					except Exception as e:
							self.fail(_UpperCAmelCase						)






			@require_torch
			@require_tokenizers
			@slow
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					from transformers import BertModel

					snake_case__	     =BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'						)						)
					snake_case__	     =BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'						)
					self._test_infer_dynamic_axis(_UpperCAmelCase					,   _UpperCAmelCase					,   'pt'						)






			@require_tf
			@require_tokenizers
			@slow
			def 						_lowercase  (     self						)							->							int:
					from transformers import TFBertModel

					snake_case__	     =TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'						)						)
					snake_case__	     =BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'						)
					self._test_infer_dynamic_axis(_UpperCAmelCase					,   _UpperCAmelCase					,   'tf'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =FeatureExtractionPipeline(_UpperCAmelCase					,   _UpperCAmelCase						)

					snake_case__	     =['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =infer_shapes(_UpperCAmelCase					,   _UpperCAmelCase						)

					# Assert all variables are present
					self.assertEqual(len(_UpperCAmelCase						)					,   len(_UpperCAmelCase						)						)
					self.assertTrue(all(var_name in shapes for var_name in variable_names						)						)
					self.assertSequenceEqual(variable_names[:3]					,   _UpperCAmelCase						)
					self.assertSequenceEqual(variable_names[3:]					,   _UpperCAmelCase						)

					# Assert inputs are {0: batch, 1: sequence}
					for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
							self.assertDictEqual(shapes[var_name]					,   {0: 'batch', 1: 'sequence'}						)

					# Assert outputs are {0: batch, 1: sequence} and {0: batch}
					self.assertDictEqual(shapes['output_0']					,   {0: 'batch', 1: 'sequence'}						)
					self.assertDictEqual(shapes['output_1']					,   {0: 'batch'}						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =['input_ids', 'attention_mask', 'token_type_ids']
					snake_case__	     ={'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
					snake_case__     ,	snake_case__	     =ensure_valid_input(FuncContiguousArgs()					,   _UpperCAmelCase					,   _UpperCAmelCase						)

					# Should have exactly the same number of args (all are valid)
					self.assertEqual(len(_UpperCAmelCase						)					,   3						)

					# Should have exactly the same input names
					self.assertEqual(set(_UpperCAmelCase						)					,   set(_UpperCAmelCase						)						)

					# Parameter should be reordered according to their respective place in the function:
					# (input_ids, token_type_ids, attention_mask)
					self.assertEqual(_UpperCAmelCase					,   (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])						)

					# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
					snake_case__     ,	snake_case__	     =ensure_valid_input(FuncNonContiguousArgs()					,   _UpperCAmelCase					,   _UpperCAmelCase						)

					# Should have exactly the one arg (all before the one not provided "some_other_args")
					self.assertEqual(len(_UpperCAmelCase						)					,   1						)
					self.assertEqual(len(_UpperCAmelCase						)					,   1						)

					# Should have only "input_ids"
					self.assertEqual(inputs_args[0]					,   tokens['input_ids']						)
					self.assertEqual(ordered_input_names[0]					,   'input_ids'						)






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =generate_identified_filename(Path('/home/something/my_fake_model.onnx'						)					,   '-test'						)
					self.assertEqual('/home/something/my_fake_model-test.onnx'					,   generated.as_posix()						)









'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__     :						int	=  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: List[Any]									=      GPTSwaTokenizer
			a_	: int									=      False
			a_	: List[Any]									=      True
			a_	: Any									=      False






			def 						_lowercase  (     self						)							->							str:
					super().setUp()

					# We have a SentencePiece fixture for testing
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase					,   eos_token='<unk>'					,   bos_token='<unk>'					,   pad_token='<unk>'						)

					tokenizer.save_pretrained(self.tmpdirname						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     ='This is a test'
					snake_case__	     ='This is a test'
					return input_text, output_text






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ='<s>'
					snake_case__	     =1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =list(self.get_tokenizer().get_vocab().keys()						)

					self.assertEqual(vocab_keys[0]					,   '<unk>'						)
					self.assertEqual(vocab_keys[1]					,   '<s>'						)
					self.assertEqual(vocab_keys[-1]					,   'j'						)
					self.assertEqual(len(_UpperCAmelCase						)					,   2000						)






			def 						_lowercase  (     self						)							->							List[str]:
					self.assertEqual(self.get_tokenizer().vocab_size					,   2000						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase						)

					snake_case__	     =tokenizer.tokenize('This is a test'						)
					self.assertListEqual(_UpperCAmelCase					,   ['▁This', '▁is', '▁a', '▁t', 'est']						)

					self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [465, 287, 265, 631, 842]						)

					snake_case__	     =tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
					# fmt: off
					self.assertListEqual(
					    _UpperCAmelCase					,   ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,   )
					# fmt: on

					snake_case__	     =tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
					self.assertListEqual(
					    _UpperCAmelCase					,   [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,   )

					snake_case__	     =tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
					# fmt: off
					self.assertListEqual(
					    _UpperCAmelCase					,   ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						)
					# fmt: on






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase						)
					snake_case__	     =['This is a test', 'I was born in 92000, and this is falsé.']
					snake_case__	     =[
					    [465, 287, 265, 631, 842],
					    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
					]

					# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
					for text, expected_ids in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						)					,   _UpperCAmelCase						)

					# Test that decode_fast returns the input text
					for text, token_ids in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						)					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =[
					    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
					    'Hey there, how are you doing this fine day?',
					    'This is a text with a trailing spaces followed by a dot     .',
					    'Häj sväjs lillebrör! =)',
					    'Det är inget fel på Mr. Cool',
					]

					# fmt: off
					snake_case__	     ={'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
					# fmt: on
					self.tokenizer_integration_test_util(
					    expected_encoding=_UpperCAmelCase					,   model_name='AI-Sweden/gpt-sw3-126m'					,   sequences=_UpperCAmelCase					,   )




